Assessing Treatment Effect Heterogeneity in Clinical Trials with Blocked Binary Outcomes

2005 ◽  
Vol 47 (5) ◽  
pp. 662-673 ◽  
Author(s):  
Jeffrey M. Albert ◽  
Gary L. Gadbury ◽  
Edward J. Mascha
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Binary Outcomes ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

scholarly journals Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP

2015 ◽  
Vol 13 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Ann A Lazar ◽  
Marco Bonetti ◽  
Bernard F Cole ◽  
Wai-ki Yip ◽  
Richard D Gelber
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

scholarly journals Dynamic borrowing in the presence of treatment effect heterogeneity

Biostatistics ◽  
2020 ◽  
Author(s):  
Ales Kotalik ◽  
David M Vock ◽  
Eric C Donny ◽  
Dorothy K Hatsukami ◽  
Joseph S Koopmeiners
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Potential Outcomes ◽  
Operating Characteristics ◽  
Treatment Effect Heterogeneity ◽  
Using Data ◽  
Effect Heterogeneity ◽  
Potential Outcomes Framework

Summary A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.

Lessons Learned from Actual Multi-Regional Clinical Trials with Signals of Treatment Effect Heterogeneity

2021 ◽  
pp. 103-127
Author(s):  
Bruce Binkowitz ◽  
Gang Li ◽  
Hui Quan ◽  
Gordon Lan ◽  
Soo Peter Ouyang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Lessons Learned ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity
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